Rheumatoid Arthritis (RA) is a chronic inflammatory and autoimmune disorder characterized by high morbidity and mortality, and profound disability. During the prior cycles of this award, we discovered that RA patients are significantly more likely to develop cardiovascular (CV) disease (i.e., myocardial infarction and heart failure) and have significantly worse outcomes after CV disease, compared to the general population. Moreover, CV disease in persons with RA showed a markedly different risk profile, with traditional CV risk factors playing a relatively less important (and sometimes paradoxical) role, and markers of inflammation/immune dysfunction playing a much greater role. Indeed, we demonstrated that CV risk prediction tools developed for the general population (e.g., Framingham) were of little value in RA, due to poor calibration (i.e., ability to predict absolute risk), and poor discrimination (i.e., ability to distinguish low from high risk). Thus, we introduced, in the current grant cycle, a novel phenotypic biomarker, immune response signatures, which (in cross sectional studies) significantly improved identification of RA subjects with silent myocardial dysfunction. The objective of this renewal application is to improve CV risk prediction for persons with RA. We propose to: 1) establish (in longitudinal studies) the utility of immune response signatures, when combined with our rich epidemiological data, for predicting myocardial function in RA, examining the impact of disease activity and biological treatment; and 2) create and validate a new, RA-specific, CV risk assessment tool. In Aim 1 we will repeat assessment of myocardial function and test the hypothesis that the 11 cytokine immune response signature (developed in the current grant cycle) is a significant predictor of future myocardial dysfunction after adjusting for demographic, disease features and CV characteristics. We will compare immune response signatures measured at 2 time points and test the hypotheses that improvement in signature is associated with lower risk for myocardial dysfunction, as well as investigating the impact of biological therapies. In Aim 2 we will devise a new, RA-specific, CV risk score by re-calibrating the Framingham, and other available CV risk scores to more accurately predict CV risk for RA; and by systematically evaluating RA disease characteristics, laboratory measures and immune response signatures for inclusion into the new CV risk score, in order to maximize discrimination. To ensure generalizability of the new score, external validation will be conducted using the large (n=25,977) and well established National Databank for Rheumatic Diseases. The creation of effective CV risk prediction tools and biomarkers will enable targeting of screening and prevention strategies towards those who could benefit the most, thereby reducing CV morbidity, mortality, and healthcare costs in persons with RA.